Methods of analyzing biological samples are generally known. In a typical analysis, a high-throughput bioassay, such as mass spectroscopy, may be performed on the biological sample to separate and quantify at least some of its constituent biochemical components (e.g. proteins, protein fragments, DNA, RNA, etc.). Based on the output of the bioassay, such as a mass spectrum, various diagnostics may be run. For example, a diagnostic model of a particular disease state may be applied to the mass spectrum to identify the sample from which the spectrum was derived as being taken from a subject that has, or does not have, the disease state. In some of the known methods of analyzing biological samples, the acquisition of the data (i.e., the performance of a high-throughput bioassay) and the analysis of the data (i.e., the application of the diagnostic model) are accomplished at the same location.
Such diagnostic models have been static, in that each such model is based on analysis of a finite set of biological samples with known attributes relevant to the disease state modeled (i.e. known to have or not to have the disease state) and is then used to assess biological samples for which the disease state is not known. Such an approach assumes that the sample set used to develop the diagnostic model is representative of the population from which unknown samples will be drawn for analysis by the model. If this assumption proves not to be valid, the model's validity and utility is questionable.
There is a need for a method of monitoring or evaluating the applicability of a diagnostic model to new, unknown biological samples and for determining whether/when a diagnostic model should be updated to reflect the differences between the original sample set and the population from which new, unknown samples have been drawn. There is further a need for generating a new model that reflects biological samples in addition to those from which the original model was created. Finally, there is a need for a method of analyzing biological samples that includes acquiring the data at a first location, transmitting a subset of the data to a second location different than the first location, and analyzing the data at the second location.